English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Machine learning models that can generalize to unseen domains are essentialwhen applied in real-world scenarios involving strong domain shifts. We addressthe challenging domain generalization (DG) problem, where a model trained on aset of source domains is expected to generalize well in unseen domains withoutany exposure to their data. The main challenge of DG is that the featureslearned from the source domains are not necessarily present in the unseentarget domains, leading to performance deterioration. We assume that learning aricher set of features is crucial to improve the transfer to a wider set ofunknown domains. For this reason, we propose COLUMBUS, a method that enforcesnew feature discovery via a targeted corruption of the most relevant input andmulti-level representations of the data. We conduct an extensive empiricalevaluation to demonstrate the effectiveness of the proposed approach whichachieves new state-of-the-art results by outperforming 18 DG algorithms onmultiple DG benchmark datasets in the DomainBed framework.

Discovery of New Multi-Level Features for Domain Generalization via Knowledge Corruption